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Related Experiment Video

Updated: Oct 18, 2025

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A Deep Learning Based Approach for Patient Pulmonary CT Image Screening to Predict Coronavirus (SARS-CoV-2)

Parag Verma1, Ankur Dumka2, Rajesh Singh3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India.

Diagnostics (Basel, Switzerland)
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

A deep learning model accurately identifies COVID-19 from CT scans, distinguishing it from influenza A and healthy cases. This AI tool aids in early screening and diagnosis of coronavirus disease 2019.

Keywords:
COVID-19convolution neural networkdeep learning modellocation attention networkmachine learningpneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The novel coronavirus (COVID-19) emerged in late 2019, rapidly spreading globally and causing significant health and economic impacts.
  • Distinguishing COVID-19 from other respiratory illnesses like influenza A is crucial for effective patient management.
  • Pulmonary computed tomography (CT) imaging offers valuable insights into respiratory conditions.

Purpose of the Study:

  • To develop and evaluate a deep learning model for identifying COVID-19 from pulmonary CT images.
  • To differentiate between COVID-19, influenza A virus infections, and healthy individuals using CT data.

Main Methods:

  • A dataset of 548 pulmonary CT images was compiled from online sources.
  • The dataset included images from 12 COVID-19 patients, 17 influenza A patients, and 15 healthy individuals.
  • Deep learning techniques were applied to analyze the CT images for pattern recognition.

Main Results:

  • The developed deep learning model achieved an accuracy of 79.39% in identifying COVID-19 cases.
  • The model demonstrated the capability to differentiate COVID-19 from influenza A and healthy controls based on CT imaging features.

Conclusions:

  • Deep learning applied to CT scans presents a promising approach for the early screening of COVID-19.
  • This analytical method can serve as a robust tool for clinical experts in diagnosing respiratory infections.